Passive multi-person location tracking utilizing signal polarization

ABSTRACT

Embodiments are directed to passive multi-person location tracking utilizing signal polarization. An embodiment of a system includes multiple receivers, including a first receiver at a first location and a second receiver at a second location, each receiver including one or more receiver antennas to receive polarized radio signals; a transmitter located at a third location to transmit polarized radio signals, the transmitter including antennas for a first signal at a first polarization direction and a second signal at a second, different polarization direction; and a processing system to receive channel state information from each receiver, and to track a plurality of individuals utilizing the received channel state information, wherein the tracking of the plurality of individuals is based at least in part on analysis of a polarization parameter and a set of location parameters generated for each of multiple reflection paths based on the channel state information.

BACKGROUND

Tracking of movement of individuals in certain locations can beessential for security and safety. While it is possible to track personsactively utilizing electronic devices, such tracking is often notpossible, and thus passive tracking may be applied. Technologies thatare designed for tracking and ranging operations, such as radar, may beapplied for tracking, but these technologies require special technologyand equipment that may be impractical in many environments.

While there are wireless technologies conventionally implemented forcommunications and data transfer, such as Wi-Fi, that may be applied intracking, existing technology cannot provide tracking when multipleindividuals are moving within a particular environment. If multiplepeople within a Wi-Fi signal range move simultaneously, the signals ofmultiple reflection paths also change, and existing technology does notallow for tracking when more than one signal path changes.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments described here are illustrated by way of example, and not byway of limitation, in the figures of the accompanying drawings in whichlike reference numerals refer to similar elements.

FIG. 1 is an illustration of passive multi-person location trackingutilizing signal polarization according to some embodiments;

FIG. 2 illustrates a system architecture for passive multi-personlocation tracking utilizing signal polarization according to someembodiments;

FIG. 3A illustrates a transmitter antenna structure for human-bodytracking according to some embodiments;

FIG. 3B illustrates a receiver antenna structure for human-body trackingaccording to some embodiments;

FIG. 4 illustrates server operation for passive multi-person locationtracking utilizing signal polarization according to some embodiments;

FIG. 5 illustrates operations in a location tracking server for passivemulti-person tracking according to some embodiments;

FIG. 6 is block diagram to illustrate an access point to provide Wi-Fitransmission or reception for passive multi-person tracking according tosome embodiments; and

FIG. 7 illustrates components of a location tracking server according tosome embodiments.

DETAILED DESCRIPTION

Embodiments described herein are directed to passive multi-personlocation tracking utilizing signal polarization.

In some embodiments, a system or process is to provide for transmissionof polarized signals and the reception of reflected signals at multipledifferent locations, the reflected signals being processed to passivelydetect and track multiple persons in a particular tracking environment.In some embodiments, the system or process utilizes a Wi-Fi transmittertransmitting at least a first signal in a first polarization directionand a second signal in a second polarization direction to enabletracking of multiple persons in the tracking environment through use ofreflected signals received at each of multiple receivers.

The wireless signal arriving at the receiver side in a system is acombination of all of the signals that traverse through multiple signalreflection paths. By processing and analyzing the channel stateinformation (CSI) provided by the Wi-Fi receiver, a system can estimatethe characteristics of the signal path, such as angle of arrival (AoA)and time of flight (ToF). Using this data, the system can analyze thelocation of the reflector and then find the position of a person whenthe person is a main source of the reflection. Such a technique iscalled wireless signal-based human body tracking.

However, conventional technology approaches are unable to track whenmultiple individuals move simultaneously in a tracking environment. Ifmultiple individuals within the Wi-Fi-signal range move simultaneously,the signals of multiple reflection paths also change. Existing trackingtechniques are designed to consider a situation in which signals from asingle path change, which is not applicable when more than one signalpath changes.

In some embodiments, a tracking technology (which may be referred toherein as WiPolar) includes a passive multi-person tracking system thatmay utilize Wi-Fi devices (including commercial off-the-shelf (COTS)devices) to employ polarimetry in passive multi-person tracking.Polarimetry refers to a measure of signal (including Wi-Fi signals)polarization, which is applied to overcome the difficulty of separatingmultiple human reflections in a tracking environment.

FIG. 1 is an illustration of passive multi-person location trackingutilizing signal polarization according to some embodiments. In someembodiments, a system or process for passive multi-person locationtracking operates as follows:

(1) Polarization Diversity 110: Polarization diversity is provided in atransmitted Wi-Fi signal that is used to illuminate a trackingenvironment. The transmitted signal may be transmitted by an apparatussuch as Wi-Fi transmitter 210 illustrated in FIG. 2, with the Wi-Fitransmitter to transmit, for example, horizontally and verticallypolarized signals.

(2) Signal Monitoring 120—A polarization parameter (polarization axialratio angle) and a set of location-based parameters (ToF, AoA, DFS(Dynamic Frequency Selection)) are continuously determined for eachreflection path on multiple Wi-Fi receivers (such as the pair of Wi-Fireceivers 220 and 225 illustrated in FIG. 2).

(3) Combining of Reflection Paths 130—The location parameters of pairsof reflection paths across the two receivers that have the closestpolarization parameter are combined to identify reflective targets.

(4) Localization of Reflective Targets 140—Each human reflective targetin the tracking environment is localized using location parameters,including angles of arrival (AoA), of the human reflection paths at eachof the multiple receivers.

The polarization of a transverse-electromagnetic (TEM) wave, such as aWi-Fi signal, is a fundamental property that defines the spatialorientation of the wave's electric field oscillation. In someembodiments, by orienting a pair of co-located antennas of the Wi-Fitransmitter in vertical and horizontal directions (or other differingdirections), a transmitter is to simultaneously generate a pair ofvertical and horizontal polarized Wi-Fi signals. In such operation, avertically oriented antenna transmits a signal in which the sinusoidaloscillations of its electric field occur perpendicular to the ground,which is referred to as vertical polarization, while a horizontallypolarized antenna transmits a signal in which the sinusoidaloscillations of its electric field occur parallel to the ground,referred to as horizontal polarization.

In some embodiments, a pair of differently polarized transmit signals,such as a first Wi-Fi signal with vertical polarization and a secondWi-Fi signal with horizontal polarization, interact with reflectors inthe tracking environment, wherein the reflectors may be humans andvarying obstacles, that possess unique horizontal and vertical radarcross-sections owing to their physical dimensions and reflectioncharacteristics. The reflected Wi-Fi signals in the tracking environmentundergo different amounts of attenuation in the horizontal and verticalpolarization before reaching one of multiple Wi-Fi receivers.

In some embodiments, the system or process measures a ratio of theamplitudes of the polarized signals in the horizontal and vertical axisat the receiver, and estimates a polarization axial ratio angle. Theestimation of the polarization axial ratio angle assists in obtaining amore accurate estimation of location-based parameters for multipleperson tracking. It may be noted that the CSI amplitude suffers fromless random noise than the phase, such as carrier frequency offset (CFO)and sampling time offset (STO), and thus the polarization angleestimated from the amplitude measurements is cleaner and more stablecompared to the other phase-based location tracking parameters such asAoA, DFS and ToF. For this reason, a system process estimates therelatively stable polarization axial ratio angle jointly with the lessstable phase-based location tracking parameters, resulting in morestable location parameters for the targets than would be generated byestimation of phase-based location tracking parameters alone. Theestimated directions of each target from the perspective of tworeceivers thus be applied to provide a confident estimate of a target'sactual location in a tracking environment.

As used herein, access point (AP) (also referred to as a wireless accesspoint (WAP)) refers to a Wi-Fi networking hardware device that allowsWi-Fi devices to connect to a wired network, including connection to theInternet or an intranet. The AP may connect to a router (via a wirednetwork) as a standalone device, or may be an integral component of therouter itself.

A Wi-Fi signal is a transverse electro-magnetic (TEM) wave comprised ofelectric and magnetic fields that oscillate in the same phase but havingtheir plane of oscillations perpendicular to each other. Assuming the EMwave propagates along the Z-axis, its electric field can oscillate atany angle γ to the XZ plane. This angle, called the polarization axialratio angle is determined by the arctangent of the ratio of theinstantaneous magnitudes of the electric field component on the Y-axisto the electric field component on the X-axis. The TEM wave is said tobe linearly polarized if γ does not vary over space or time. Formally,the instantaneous electric field vector {right arrow over (E)} of a TEMwave Ae^(−j(2+ft−kz)) of frequency fat time t and spatial position z(i.e., along the Z-axis) and spatial phase factor

$k = {{2\pi \text{/}\lambda k} = \frac{2\pi}{\lambda}}$

can be represented as the vector sum of two components along the X and Yaxes, as:

{right arrow over (E)}={right arrow over (e)} _(x) E _(x) +{right arrowover (e)} _(y) E _(y)  [1]

Where:

{right arrow over (e)}_(x) and {right arrow over (e)}_(y)=unit vectorsalong the X-axis and Y-axis, respectively

E_(x)=A_(x)e^(−j(2πft−kz))=the X-component of {right arrow over (E)}

E_(y)=A_(y)e^(−j(2πft−kz+ϕ))=the Y-component of {right arrow over (E)}

ϕ=the phase difference between the X and Y components.

The polarization axial ratio angle γ is thus given by:

$\begin{matrix}{\gamma = {\arctan ( \frac{E_{y}}{E_{x}} )}} & \lbrack 2\rbrack\end{matrix}$

Depending on the values of A_(x), A_(y), and ϕ, three types ofpolarization can be present: linear polarization (LP), circularpolarization (CP), and elliptical polarization (EP). In linearpolarization, the value of ϕ is always zero, and thus the orientation ofthe plane of the electric field does not change during the propagationof the TEM irrespective of the value of A_(x) and A_(y). Further, thereare two special cases of linear polarization: vertical polarization andhorizontal polarization. A TEM wave is said to be vertically polarizedif ϕ=0, A_(x)=0, and A_(y)≠0, and horizontally polarized if ϕ=0,A_(x)≠0, and A_(y)=0. However, if ϕ≠0, the X-component will lead (forpositive ϕ) or lag the Y-component (for negative ϕ) over time, resultingin the tip of the electric field vector E to be described as an ellipseas the TEM wave propagates, for which the TEM wave is said to beelliptically polarized. Further, a special case of ellipticalpolarization, referred to as circular polarization, occurs when theamplitudes of the X and Y components are equal and orthogonal (i.e.,A_(x)=A_(y)) and ϕ=π/2, referred to as right-hand circular polarization(RHCP) (or if A_(x)=A_(y) and ϕ=−π/2, referred to as Left-Hand CircularPolarization (LHCP)).

FIG. 2 illustrates a system architecture for passive multi-personlocation tracking utilizing signal polarization according to someembodiments. In some embodiments, a Wi-Fi transmitter access point (TX)210 (also referred to herein as a Wi-Fi transmitter) located in atracking environment transmits Wi-Fi packets at a certain fixed rate.The tracking environment 200 may be any physical environment, but inparticular may include a building or campus, for example a medical,industrial, or educational institution. The Wi-Fi signal (S) of a packetfrom the transmitter is generated by a pair of spatially collocatedpolarized antennas, and in particular may be horizontally and verticallypolarized antennas, such as illustrated in FIG. 4A. The signal operatesto illuminate the tracking environment 200 simultaneously along Fnarrowband frequencies, which are referred to as subcarriers.

The transmitted signal of each subcarrier propagates in both of thehorizontal and vertical polarizations along L paths from the transmitterto multiple Wi-Fi receivers, illustrated as a first Wi-Fi receiveraccess point (R₁) 220 and a second Wi-Fi receiver access point (R₂) 230(also referred to herein as a Wi-Fi receiver), each Wi-Fi receiverincluding a uniform linear array (ULA) of antennas, such as illustratedin FIG. 3B. Each of the L paths can interact with stationary and movingobjects in the tracking environment 200, such as walls (or otherstructures), obstacles, and human targets of interest, and consequentlyundergo change in amplitude and phase in both horizontal and verticalpolarized components. The antennas of receivers may be located a certaindistance D apart, where D is at least one wavelength.

For example, the tracking environment 200 may include multiple personswho may be in motion, the persons in one example including a firstperson 240 and a second person 245, each of whom may be moving in anydirection along any path. Wi-Fi receivers 220 and 230 thus each canreceive reflected Wi-Fi signals from both of the persons 240 and 245, aswell as reflected signals from any other structure, obstacle, or personin the tracking environment. The signals received at Wi-Fi receiver 220include a signal (Φ₁ ^(R) ¹ ) reflected from person 240 and a signal (Φ₂^(R) ¹ ) reflected from person 245, and the signals received at Wi-Fireceiver 230 include a signal (Φ₁ ^(R) ² ) reflected from person 240 anda signal (Φ₂ ^(R) ² ) reflected from person 245.

In some embodiments, the signals received at Wi-Fi receivers 220-230 areanalyzed to locate and track the persons 240-245 in the trackingenvironment. In some embodiments, the analysis is performed by aprocessing system, such as location tracking server 450 illustrated inFIG. 4. The processing system may include a Wi-Fi network controller ora dedicated processing server. However, in other embodiments theanalysis may be provided using a different structure, including the usemultiple processing systems, processing by remote cloud systems, orprocessing at least in part within one or more of the Wi-Fi receivers.It is further noted that the Wi-Fi transmitter 210 and the Wi-Fireceivers 220-230 may include both transmission and receptioncapability, and may include other wireless and wired communicationcapabilities.

In some embodiments, a tracking system may be expanded tothree-dimensional (3D) operation. The system architecture in suchoperation may further including one or more additional receivers thatare a certain distance from Wi-Fi receivers 220 and 230 along a thirdaxis such as a Z-axis if it is assumed that FIG. 2 illustrates thesystem elements along X- and Y-axis.

FIG. 3A illustrates a transmitter antenna structure for human-bodytracking and FIG. 3B illustrates a receiver antenna structure forhuman-body tracking according to some embodiments. As shown in FIG. 3A,a transmitter (TX) antenna structure 300 includes TX antenna-1 totransmit a horizontally polarized signal and TX antenna-2 to transmit avertically polarized signal. In some embodiments, TX antenna-1 and TXantenna-2 are co-located at a Wi-Fi transmitter, such as Wi-Fitransmitter 210 illustrated in FIG. 2. However, the arrangement of TXantenna-1 and TX antenna-2 in relation to each other may vary from theillustration provided in FIG. 3A depending on the antenna designutilized for the transmitter.

As shown in FIG. 3B, a receiver (RX) antenna structure 350 for a Wi-Fireceiver, such as an antenna for each of Wi-Fi receivers 220 and 230illustrated in FIG. 2, includes a uniform linear array (ULA) of antennasto receive the reflected horizontally polarized signals and verticallypolarized signals. As illustrated in FIG. 3B, the antennas of the ULAmay be a set (such as three) right-hand circular polarization (RHCP)antennas. Alternatively, the antennas of the ULA may be a set left-handcircular polarization (LHCP) antennas, with the calculations hereinbeing modified accordingly.

FIG. 4 illustrates server operation for passive multi-person locationtracking utilizing signal polarization according to some embodiments.While for simplicity FIG. 4 illustrates a single, separate locationtracking server 450, which may include a Wi-Fi network controller or adedicated processing server. However, in other embodiments the trackinganalysis may be provided using a different structure, including the usemultiple processing systems, processing by remote cloud systems, orprocessing at least in part within one or more of the Wi-Fi receivers.

In some embodiments, a processing system, referred to herein as alocation tracking server 450, receives channel state information (CSI)from multiple Wi-Fi receivers, such as a first receiver (R₁) 420 andsecond receiver (R₂) 430, which may be Wi-Fi receivers 220 and 230 asillustrated in FIG. 2. The receivers are to receive signals includingmultiple reflected polarized signals from a Wi-Fi transmitter, such asWi-Fi transmitter 210 illustrated in FIG. 2.

In some embodiments, the location tracking server 450 is to process theCSI from the receivers 420-430 to provide for location tracking ofmultiple persons within a tracking environment, such as persons 240 and245 in tracking environment 200 in FIG. 2, using a location trackingalgorithm 460 to perform path parameter estimation, path pairing and AoAestimation, and location estimation for human reflective targets.

The degree of change in amplitude and phase for each path varies inaccordance with the electrical and geometrical properties of itsinteracting objects before reaching the receiver. At each receiver420-430, the received wireless signal Y will comprise the signals fromall the L paths. For each packet transmitted with the signal S, thereceived signal Y is given by Y=HS+N, where H represents the CSI and Nrepresents the noise. Because the Wi-Fi network interface card (NIC) ofthe receiver measures CSI separately for each transmit antenna, receiveantenna, subcarrier frequency, and packet sent, the CSI H can be denotedas H(p,r,f,t) where p represents a sensor in the polarization domain(transmit antenna), r represents a sensor in the spatial domain (receiveantenna), f represents a sensor in the frequency domain (i.e.,subcarrier), and t represents a sensor in the time domain (packetnumber). Thus, a CSI measurement for transmit antenna p, receive antennar, and subcarrier f at time t is given by:

H(p,r,f,t)=Σ_(l=1) ^(L)α_(l) ^(p)(r,f,t)e ^(−jΦ) ^(l) ^((r,f,t))  [3]

In Equation [3], α_(l) ^(p)(r, f, t) represents the complex attenuationweight for the signal from transmit antenna p along path l to thereceiver at sensor (r, f, t), and Φ_(l)(r, f, t) represents the phase ofthe same path l at sensor (r, f, t). Taking the first space sensorr_(o), the first subcarrier f_(o), and the first-time sensor t_(o) asreference, Φ_(l) (r, f, t) is defined by the AoA of the path Φ_(l), theDFS of the path ν_(l), and ToF of the path τ_(l) as:

$\begin{matrix}{{\Phi_{l}( {r,f,t} )} = {{2\pi \tau_{l}f_{0}} + {2{\pi( {{\tau_{l}( {f - f_{0}} )} - \frac{f{\nu_{l}( {t - t_{0}} )}}{c} + \frac{\begin{matrix}{f_{0}{\delta ( {r,r_{0}} )}} \\{\sin (\varphi)}\end{matrix}}{c}} )}}}} & \lbrack 4\rbrack\end{matrix}$

where δ(r, r₀) represents the physical distance between the spatialsensors r and r₀, and c is the speed of light.

In some embodiments, two approximations may be made for Equation [3]:

First, considering a receive antenna array of a few elements (e.g., 3elements, such as illustrated in FIG. 3B), a short observation timewindow of a fraction of a second (e.g., 0.2 seconds), and a narrowbandwidth of operation (e.g., 20 MHz), the complex attenuation weightα_(l) ^(p)(r, f, t) for all sensors in the space, time, and frequencydomain can be approximated as a constant α_(l) ^(p).

Second, because the two transmit antennas are co-located (such asillustrated in FIG. 3B), the time of flight (ToF) of each path l will beapproximately the same in the CSI for both transmit antennas in all thethree domains of space, time and frequency. Further, the measured CSI inpractice still contains some background white Gaussian noise N(r,f,t).Therefore, the measured CSI at time t for receive antenna r andsubcarrier f can be approximately combined with the noise as:

H(r,f,t)≈Σ_(l=1) ^(L) A _(l) e ^(−jΦ) ^(l) ^((r,f,t)) +N(r,f,t)  [5]

Where A_(l)=[a_(l) ^(v),a_(l) ^(h)] represents a polarization matrixwhose elements a_(l) ^(v) and a_(l) ^(h) represent the complexattenuation weights for the vertical and horizontal polarized transmitsignals measured by the sensor array at the receiver, respectively. Thecomplex attenuation weights for the vertical and horizontal polarizedtransmit signals can be jointly expressed as the polarization axialratio

$( {{i.e.},\ {\gamma_{l} = {\arctan ( \frac{a_{l}^{\nu}}{a_{l}^{h}} )}}} ).$

Thus, with the definition of γ_(l) and Φ₁(r, f, t) being as providedabove, each reflection path l is characterized by its complex weightmatrix, a polarization axial ratio angle, an AoA, a ToF, and DFS,denoted together as a path parameter vector θ_(l)=[A_(l), γ_(l), ϕ_(l),τ_(l), ν_(l)].

With the system as illustrated in FIGS. 2-4, the signal model providedin Equation [5], and the above characterization of path parameters, asystem or process is to apply a machine learning (ML) estimationframework to accurately determine the ϕ_(l) for all paths l∈L includingthe paths reflected from human body that lie along some path k∈L, sothat each target can first be potentially identified by its γ_(k) value,and then located by its AoA at two spatially separate Wi-Fi receivers.In some embodiments, a system process is to jointly estimate theparameter vector for the multiple paths at each receiver separately, byextending the ML based SAGE algorithm, the extended algorithm referredto herein as the polarization-SAGE algorithm.

Joint Maximum Likelihood Estimation of Path Parameters: In someembodiments, the polarization-SAGE algorithm is applied for jointestimation of path parameters of all L multi-paths. There is presumed afinite time duration T over which a Wi-Fi receiver records the channelmatrix H from T packets of dimensions 2×R×F×T as modeled by Equation[5]. In such estimation, let Θ=[θ₁, θ₂, . . . , θ_(L)] containing theparameters of all L paths, and let κ=(κ_(r), κ_(f), κ_(t)) such thatκ_(r)∈[1, R] κ_(f)∈[1, F], κ_(t) ∈[1, T] is a valid combination ofsensors in the domains of space, frequency and time respectively, suchthat K_(r)=1 represents the reference sensor R_(o), κ_(f)=1 representsthe reference sensor F_(o), and κ_(t)=1 represents the reference sensorT_(o).

Equation [5] may then be rewritten as:

H(κ)≈Σ_(l=1) ^(L) s(κ;θ_(l))+N(κ)  [6]

Where s(κ;θ_(l))=A_(l)e^(−jΦ) ^(l) ^((r,f,t)).

With the above formulation in Equation [6], the SAGE algorithm may beapplied to perform maximum likelihood estimation (MLE) of Θ. It may benoted that the SAGE algorithm relies on two sets of data: incomplete,observable data and complete, unobservable data. The algorithm considersa measured CSI H(κ) as the incomplete, observable data. Further, itconsiders each individual multipath component in the CSI, corrupted by apart of the total additive noise N as the complete, unobservable data.Denoting the observable data by the vector X(κ)=[X₁(κ), X₂(κ), . . . ,X_(l)(κ)], the l-th multipath component of X(κ) is given by:

$\begin{matrix}{{X(\kappa)} = {{s( {\kappa;\theta_{l}} )} + {\sqrt{\frac{\beta_{l}}{2}}{N(\kappa)}}}} & \lbrack 7\rbrack\end{matrix}$

Where β_(l)∈[0,1]. Therefore, H(κ) from Equation [6] can also be writtenas H(κ)=Σ_(l=1) ^(L)X_(l)(κ), i.e., the incomplete, observable data CSIH(κ) is a sum of all complete, unobservable data in X(κ).

Given the above SAGE-compatible definitions of H(κ) and X_(l)(κ), theobjective of the polarization-SAGE algorithm is to obtain the maximumlikelihood estimation of the path parameter θ_(l) given the observabledata X_(l) (i.e., X_(l)(κ) for all possible κ). However, because thecomplete data X_(l) for each path is unobservable, the complete data hasto be first estimated from all the observed, H(κ) using a prior estimateof all paths {circumflex over (Θ)}′, before obtaining the MLE of θ_(l).Thus, the polarization-SAGE algorithm proceeds in the following stages:

(1) Initialization: The algorithm initializes a value for L, a priorestimate {circumflex over (Θ)}′.

(2) Expectation and Maximization: The algorithm proceeds iteratively intwo stages for each path l in L: (i) Expectation for finding {circumflexover (X)}_(l), an estimate of X_(l) for the current iteration and (ii)Maximization for finding the MLE of Θ′ for the current iteration.

(3) Convergence: The algorithm checks for convergence at the end of eachiteration, i.e., after running Expectation and Maximization on allpaths. If the algorithm converges, it outputs the estimated pathparameters of all paths from the current iteration. If convergence isnot met, the algorithm continues with the next iteration of Expectationand Maximization.

The Initialization, Expectation, Maximization, and Convergence stagesare more fully detailed below. In such explanation, the [{circumflexover (.)}] superscript is used to indicate an estimated value for thecurrent iteration and [{circumflex over (.)}′] superscript is used toindicate an estimated value obtained from a previous iteration.

Initialization: The polarization-SAGE algorithm sets L to a value thatcovers all of the dominant multi-paths, which are usually 4 to 5 inindoor environments plus the maximum number of human targets. Next, thealgorithm sets the prior estimate {circumflex over (Θ)}′ to all zerosand initializes an iteration counter μ=1 along with a finite limitμ_(max) that limits to the maximum number of iterations in theExpectation process to μ_(max) irrespective of attaining convergence.Further, the algorithm defines a convergence threshold value of ∈_(max)in the same 5-Dimensional space as Θ to detect convergence of theiterations in the Expectation process. Finally, it proceeds to thetwo-stage iterations of Expectation and Maximization.

Expectation: In this stage, the polarization-SAGE algorithm estimatesthe complete, unobservable data

using the incomplete, observable CSI data H(κ) and the estimate from theprevious iteration

=[

,

, . . . ,

] by taking the conditional expectation:

{circumflex over (X)} _(l)(κ;

)=s(κ;

)+β_(l)[H(κ)−Σ_(l=1) ^(L) s(κ;

)]  [8]

In Equation [8], the first summation term in the right hand side (RHS)stands for the contribution of the specific multipath component, and thesecond summation term in the RHS stands for an estimate of the additivenoise, imputed by subtracting all estimated wave contributions from thereceived signal. The value of β_(l) is set to 1, as it minimizes theconvergence rate of the algorithm.

Maximization: The polarization-SAGE algorithm estimates the parametersof the multipath component θ_(l) using the corresponding complete data{circumflex over (X)}_(l) estimated by the Expectation stage. Note thatthe parameters of θ_(l) can be determined by computing the MLE of thelog-likelihood function Λ(θ_(l); X_(l)) given by ({circumflex over(θ)}_(l))_(ML)({circumflex over (X)}_(l))=arg max{Λ(θ_(l); X_(l))}, forwhich the log-likelihood function Λ(θ_(l); X_(l)) is defined as:

Λ(θ_(l) ;X _(l))=2∫_(κ) ^(□) R{s ^(H)(κ;θ_(l))X _(l)(κ)}dκ−∫ _(κ) ^(□)∥s(κ;θ_(l))∥²  [9]

With R{.}, H, and ∥.∥ denoting the real part, Hermitian transpose, andnorm of a complex matrix, respectively.

The above approach is generally computationally infeasible due to highdimensionality of Θ (i.e., 5L). However, if the integral in Equation [9]is approximated by the discrete sample summation over all values of xalong with the substitution of s(κ; θ_(l))=A_(l)e^(−jΦ) ^(l) ^((κ)), theMLE estimate ({circumflex over (θ)}_(l))_(ML)({circumflex over (X)}_(l))may be shown to be equal to the values of θ_(l) that maximizes the costfunction z whose parameters are only the phase-dependent parameters ofθ_(l) (i.e., [ϕ_(l), τ_(l), ν_(l)]), and the estimated observed data{circumflex over (X)}_(l). In other words,

${( {\hat{\theta}}_{l} )_{ML}( {\hat{X}}_{l} )} \equiv {\arg {\max\limits_{\varphi_{l},\tau_{l},v_{l}}{\{ {z( {\varphi_{l},\tau_{l},{v_{l};{\hat{X}}_{l}}} )} \}.}}}$

The cost function z(ϕ_(l), τ_(l), ν_(l); {circumflex over (X)}_(l)) hasa structure similar to Equation [4], but is opposite in the sign of theexponent of equation [3], given by:

$\begin{matrix}{{z( {\varphi_{l},\tau_{l},{v_{l};{\hat{X}}_{l}}} )} = {\sum\limits_{r = r_{0}}^{R}\; {\sum\limits_{f = f_{0}}^{F}\; {\sum\limits_{t = t_{0}}^{T}\; {{{\hat{X}}_{l}( {r,f,t} )}\exp {\quad\lbrack {j\; 2\; {\pi ( {{\tau_{l}( {f - f_{0}} )} - \frac{f\; {v_{l}( {t - t_{0}} )}}{c} + \frac{f_{0}{\delta ( {r,r_{0}} )}{\sin (\varphi)}}{c}} )}} \rbrack}}}}}} & \lbrack 10\rbrack\end{matrix}$

Thus, in the Maximization stage, the polarization-SAGE algorithm firsttakes the estimated {circumflex over (X)}_(l) from the Expectation stageand finds the solution of

$\arg {\max\limits_{\varphi_{l},\tau_{l},v_{l}}{\{ {z( {\varphi_{l},\tau_{l},{v_{l};{\hat{X}}_{l}}} )} \}.}}$

Because the solution of

$\arg {\max\limits_{\varphi_{l},\tau_{l},v_{l}}\{ {z( {\varphi_{l},\tau_{l},{v_{l};{\hat{X}}_{l}}} )} \}}$

is a three-dimensional optimization problem and computationallyintensive, WiPolar follows SAGE by reducing the computational complexityof three-dimensional optimization by performing three successiveone-dimensional procedures, where each of the three phase-based pathparameters (i.e., ϕ_(l), τ_(l), ν_(l)) for the current iteration isestimated sequentially as:

$\begin{matrix}{\overset{\hat{}}{\tau_{l}} = {\arg {\max\limits_{\tau}{{z( {{\overset{\hat{}}{\varphi}}_{l}^{\prime},\tau,{{\overset{\hat{}}{v}}_{l}^{\prime};{\hat{X}}_{l}}} )}}}}} & \lbrack 11\rbrack \\{{\overset{\hat{}}{\varphi}}_{l} = {\arg {\max\limits_{\varphi}{{z( {\varphi,\overset{\hat{}}{\tau},{{\overset{\hat{}}{v}}_{l}^{\prime};{\hat{X}}_{l}}} )}}}}} & \lbrack 12\rbrack \\{{\overset{\hat{}}{v}}_{l} = {\arg {\max\limits_{v}{{z( {{\overset{\hat{}}{\varphi}}_{l}^{\prime},\overset{\hat{}}{\tau},{v_{l};{\hat{X}}_{l}}} )}}}}} & \lbrack 13\rbrack\end{matrix}$

Finally, with the above three estimated phase-based parameters{circumflex over (ϕ)}_(l), {circumflex over (τ)}_(l), and {circumflexover (ν)}_(l), and the 4-Dimensional matrix {circumflex over (X)}_(l)(i.e., {circumflex over (X)}_(l) ∈R^(2×R×F×T)), the algorithm'sMaximization stage estimates the attenuation weights for the verticaland horizontal polarized transmit signals along with their polarizationaxial ratio parameter {circumflex over (γ)}_(l) as:

$\begin{matrix}{\lbrack {{\overset{\hat{}}{\alpha}}_{V},{\overset{\hat{}}{\alpha}}_{H}} \rbrack = \frac{z( {{\hat{\varphi}}_{l},{\hat{\tau}}_{l},{{\hat{v}}_{l};{\hat{X}}_{l}}} )}{RFT}} & \lbrack 14\rbrack \\{{\overset{\hat{}}{\gamma}}_{l} = {\arctan ( \frac{{\overset{\hat{}}{\alpha}}_{V}}{{\overset{\hat{}}{\alpha}}_{H}} )}} & \lbrack 15\rbrack \\{{\overset{\hat{}}{A}}_{l} = \lbrack {{\overset{\hat{}}{\alpha}}_{V},{\overset{\hat{}}{\alpha}}_{H}} \rbrack} & \lbrack 16\rbrack\end{matrix}$

Convergence: After the Maximization stage of each iteration, theabsolute difference ∈ between the latest path parameter estimates{circumflex over (Θ)} and the previous iteration path parameterestimates {circumflex over (Θ)}′ is compared against the threshold∈_(max). The algorithm is considered to have converged if ∈<∈_(max) andthe latest path parameter estimates {circumflex over (Θ)} are output bythe algorithm. If convergence is not met, the iteration counter μincreases by 1 and the algorithm proceeds to another iteration of thesecond step unless μ=μ_(max), for which the latest path parameterestimates {circumflex over (Θ)} are output as the result of thePolarization-SAGE algorithm for the next step of mobile pathidentification and matching.

Localization of Human Targets: In some embodiments, the WiPolar systemor process is to track the position of each person in a trackingenvironment using the path parameters from two (or more) Wi-Fi receiversR₁ and R₂. Given L estimated path parameters for two receivers Θ^(R) ¹and Θ^(R) ² , a system or process is first to match pairs of pathparameters for each human location. Then, the system or process is tofind the location of each person utilizing the AoAs of each reflectionpath. Further details regarding Path Pairing and Localization mayinclude the following:

(1) Path Pairing: In the Path Pairing process, for each time window x,the system or process is to consider all possible pairs of pathparameter vectors <θ_(i), θ_(j)> such that θ_(i)∈Θ_(x) ^(R) ¹ andθ_(j)∈Θ_(x) ^(R) ² , and assigns a score that represents the likelihoodof a match being a combination of path parameters at the two receiversthat arise from the l-th reflector in the tracking environment.

To score all the pairs, the system or process is to leverage thepolarization axial ratio angle. Because each reflector (such as humansubject) has a unique horizontal and vertical radar cross sectionirrespective of its location, the ratio of the target's reflected signalfor the vertical polarized transmit signal to that of the horizontalpolarized transmit signal remain largely static for small amount of time(e.g., 0.2 second). Therefore, a specific reflector will yield a similarpolarization axial ratio angle at both receivers, and consequently, thereflector's AoAs at both the receivers can be combined with the knownposition of the receivers to derive the location of the reflector. Thesystem or process is to devise its similarity score for a given pair<θ_(i), θ_(j)> on the basis of their amplitude in vertical polarization({circumflex over (α)}_(V)) and the polarization axial ratio angle γ.Specifically, for θ_(i)=[α_(V) ^(i),α_(H) ^(i),γ_(i),ϕ_(i),τ_(i),ν_(i)]and θ_(j)=[α_(V) ^(j),α_(H) ^(j),γ_(j),ϕ_(j),τ_(j),ν_(j)], the system orprocess is to compute the similarity score

${\sigma_{ij} = \frac{{\epsilon\alpha}_{V}^{i}\alpha_{V}^{j}}{d_{2}( {\gamma_{i},\gamma_{j}} )}},{{where}\mspace{14mu} {d_{2}( \cdot )}}$

represents the Euclidean distance and E represents a weight for theproduct of the vertical polarization magnitudes. In a particularimplementation, there is an empirical setting of ∈=0.2. Considering upto two dominant static multipaths and K human reflection paths, thesystem or process then selects all the AoA pairs Ω=<ϕ_(i) ¹,ϕ_(j) ¹>,<ϕ_(i) ²,ϕ_(j) ²>, . . . , <ϕ_(i) ^(K+2),ϕ_(j) ^(K+2)> of top K+2 pairsof <θ_(i),θ_(j)> in decreasing order of their similarity score σ_(ij)for localization.

(2) Localization of Human Targets: The system or process is then to usethe top K+2 AoA pairs in Ω from each time window to locate the humantargets in a tracking environment. Given the distance D between thereceivers R₁ and R₂ and the pair of AoAs at R₁ and R₂ [ϕ_(a), ϕ_(b)]∈Ω,the system or process is to output a 2D Location pair l=(x,y) using theTriangle sine rule as:

${x = {D \times \frac{\sin ( \varphi_{a} )}{\sin ( {\pi - ( {\varphi_{a} + \varphi_{b}} )} )}}},{y = {D \times {\frac{\sin ( \varphi_{b} )}{\sin ( {\pi - ( {\varphi_{a} + \varphi_{b}} )} )}.}}}$

Thus, the system or process converts the set of aggregated AoA pairs Ωinto a set of 2D Location pairs L. The system or process then runs aclustering algorithm to identify groups of the output locations. Becausethe human paths are relatively more dynamic than static paths even whenstanding still due to subtle movements such as breathing, the clusterscorresponding to the static paths will be tighter than the humanreflected paths. Because the input data for clustering is generated byaggregating the angle parameters of K+2 paths, the number of expectedclusters will be K+2. Given the above expected number of clusters, alongwith the values of L in the two-dimensional Cartesian space, the systemor process uses the K-Means algorithm with the Euclidean distance metricand K+2 as the number of clusters to cluster the locations in L.

FIG. 5 illustrates operations in a location tracking server for passivemulti-person tracking according to some embodiments. FIG. 5 illustratesan overall multi-person tracking system operation, as more fullydescribed above.

As shown in FIG. 5, the CSIs collected from each Wi-Fi receiver, such asa first receiver 505 (R₁) and a second receiver 507 (R₂), are deliveredto a location tracking server 500, which may include a Wi-Fi networkcontroller, a dedicated processing server, or other processing elementor combination of processing elements. From the CSI for each receiver,parameter estimation is performed 510, the path parameter vector Θ beingindependently estimated using the Expectation Maximization algorithmdescribed above (Polarization-SAGE). In some embodiments, the pairs ofpath parameter vectors <θ_(i), θ_(j)> and the corresponding AoA pairs Ωare selected during a path-pairing process 515. The AoA pairs then areconverted into positions of the human subjects within the trackingenvironment in 2D space 520. The resulting data may then be delivered toa higher-level application, including, but not limited to, a securitysurveillance system.

FIG. 6 is block diagram to illustrate an access point to provide Wi-Fitransmission or reception for passive multi-person tracking according tosome embodiments. An access point includes additional components andelements not illustrated in FIG. 6, which is simplified for sake ofillustration. The illustrated Wi-Fi transmitter or receiver 600 mayinclude an access point operating under one or more IEEE 802.11standards, such as Wi-Fi access point transmitter 210 or Wi-Fi accesspoint receiver 220-230 illustrated in FIG. 2.

In some embodiments, the Wi-Fi transmitter or receiver 600 includes aprocessing unit 605, a transmitter and/or receiver 610, power control615, and one or more antennas 620 for wireless signal communication. Insome embodiments, the one or more antennas 620 include at least one of apair of co-located polarized transmission antennas for a Wi-Fitransmitter, as illustrated in FIG. 3A, or a uniform linear array ofantennas for a Wi-Fi receiver, as illustrated in FIG. 3B. The Wi-Fitransmitter or receiver 600 may further include one or more ports 625for network connections or other connections, and a memory 630 forstorage of data, which may include volatile and nonvolatile memory(including flash memory and similar elements), registers, and otherstorage technologies.

In some embodiments, the Wi-Fi transmitter or receiver 600 may furtherincludes firmware or hardware or both 650 to provide control for passivemulti-person tracking operation, such as illustrated in FIGS. 1-5.

FIG. 7 illustrates components of a location tracking server according tosome embodiments. In some embodiments, a location tracking server 700may include a Wi-Fi network controller or a dedicated processing server,such as location tracking server 450 illustrated in FIG. 4 The locationtracking server 700 may include a system board 702 (which may also bereferred to as a motherboard, main circuit board, or other terms). Theboard 702 may include a number of components, including but not limitedto a processor 704, such as a central processing unit (CPU), and atleast one communication package or chip 706. The communication package706 may be coupled to one or more antennas 716. The processor 704 isphysically and electrically coupled to the board 702.

In some embodiments, the location tracking server 700 includes one ormore ports 740 for connection to multiple Wi-Fi receivers, illustratedas a first receiver (R₁) 760 and a second Wi-Fi receive (R₂) 765, suchas Wi-Fi receivers 220-230 illustrated in FIG. 2. In some embodiments,the location tracking server 700 includes implementation of a locationtracking algorithm 730, which may be at least partially implemented infirmware (FW) 712, to provide for passive multi-person location trackingutilizing signal polarization. The location tracking algorithm 730 mayoperate as illustrated in FIG. 5 for location tracking server 500,including path parameter estimation based on CSI from Wi-Fi receivers760-765, path pairing and AoA estimation, and location estimation.

The location tracking server 700 may include other components that mayor may not be physically and electrically coupled to the board 702.These other components include, but are not limited to, volatile memory(e.g., DRAM) 708, nonvolatile memory (e.g., ROM) 710, flash memory (notshown), a mass memory 714 (such as a solid state drive (SSD) or harddrive), a graphics processor 716, a digital signal processor (notshown), a crypto processor (not shown), a chipset 718, a power amplifier720. These components may be connected to the system board 702, mountedto the system board, or combined with any of the other components.

The communication package 706 enables wireless and/or wiredcommunications for the transfer of data to and from the locationtracking server 700. The term “wireless” and its derivatives may be usedto describe circuits, devices, systems, methods, techniques,communications channels, etc., that may communicate data through the useof modulated electromagnetic radiation through a non-solid medium. Theterm does not imply that the associated devices do not contain anywires, although in some embodiments they might not. The communicationpackage 706 may implement any of a number of wireless or wired standardsor protocols, including but not limited to Wi-Fi (IEEE 802.11 family),WiMAX (IEEE 802.16 family), IEEE 802.20, long term evolution (LTE),Ev-DO (Evolution Data Optimized), HSPA+, HSDPA+, HSUPA+, EDGE EnhancedData rates for GSM evolution), GSM (Global System for Mobilecommunications), GPRS (General Package Radio Service), CDMA (CodeDivision Multiple Access), TDMA (Time Division Multiple Access), DECT(Digital Enhanced Cordless Telecommunications), Bluetooth, Ethernetderivatives thereof, as well as any other wireless and wired protocolsthat are designated as 3G, 4G, 5G, and beyond. The location trackingserver 700 may include a plurality of communication packages 706. Forinstance, a first communication package 706 may be dedicated to shorterrange wireless communications such as Wi-Fi and Bluetooth and a secondcommunication package 706 may be dedicated to longer range wirelesscommunications such as GSM, EDGE, GPRS, CDMA, WiMAX, LTE, Ev-DO, andothers.

Embodiments may be implemented using one or more memory chips,controllers, CPUs (Central Processing Unit), microchips or integratedcircuits interconnected using a motherboard, an application specificintegrated circuit (ASIC), and/or a field programmable gate array(FPGA). The term “logic” may include, by way of example, software orhardware and/or combinations of software and hardware.

The following clauses and/or examples pertain to further embodiments orexamples. Specifics in the examples may be applied anywhere in one ormore embodiments. The various features of the different embodiments orexamples may be variously combined with certain features included andothers excluded to suit a variety of different applications. Examplesmay include subject matter such as a method, means for performing actsof the method, at least one machine-readable medium, such as anon-transitory machine-readable medium, including instructions that,when performed by a machine, cause the machine to perform acts of themethod, or of an apparatus or system for facilitating operationsaccording to embodiments and examples described herein.

In some embodiments, a system includes a plurality of receivers,including a first receiver at a first location and a second receiver ata second location, each of the plurality of receivers including one ormore receiver antennas to receive polarized radio signals; a transmitterlocated at a third location to transmit a plurality of polarized radiosignals, the transmitter including antennas for transmission of a firstsignal at a first polarization direction and transmission of a secondsignal at a second, different polarization direction; and a processingsystem to receive channel state information from each of the pluralityof receivers, and to track a plurality of individuals in a regionutilizing the received channel state information, wherein the trackingof the plurality of individuals is based at least in part on analysis ofa polarization parameter and a set of location parameters that aregenerated for each of plurality of reflection paths based on the channelstate information for each of the plurality of receivers.

In some embodiments, one or more non-transitory computer-readablestorage mediums have stored thereon executable computer programinstructions that, when executed by one or more processors, cause theone or more processors to perform operations including receiving channelstate information for received signals from each of a plurality ofreceivers, the plurality of receivers including a first receiver and asecond receiver, the received signals being reflections of a firstsignal transmitted at a first polarization direction and a second signaltransmitted at a second polarization direction; determining apolarization parameter and a set of location parameters for each of aplurality of reflection paths at each of the plurality of receivers;combining reflection paths across the plurality of receivers to identifyreflections for a plurality of persons; and determining a location foreach of the plurality of persons based at least in part on the set oflocation parameters for each of the plurality of reflection paths ateach of the plurality of receivers.

In some embodiments, method for passive multi-person location trackingutilizing signal polarization includes transmitting polarized radiosignals from a Wi-Fi transmitter at a first location, the polarizedsignals including a first signal transmitted with a horizontalpolarization and a second signal transmitted with vertical polarization,the signals being transmitted in a tracking environment; receivingreflected signals from the tracking environment at a first receiver at afirst location and a second receiver at a second location, each of thefirst and second receivers including one or more receiver antennas toreceive polarized radio signals; receiving channel state information ata location tracking server from the first receiver and the secondreceiver; generating a polarization parameter and a set of locationparameters based on the channel state information received from thefirst receiver and the second receiver; combining of reflection paths atfirst and second receivers that have a closest polarization parameter toidentify a plurality of persons in the tracking environment; anddetermining a location of each of plurality of persons in the trackingenvironment using the set of location parameters.

In the description above, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the described embodiments. It will be apparent,however, to one skilled in the art that embodiments may be practicedwithout some of these specific details. In other instances, well-knownstructures and devices are shown in block diagram form. There may beintermediate structure between illustrated components. The componentsdescribed or illustrated herein may have additional inputs or outputsthat are not illustrated or described.

Various embodiments may include various processes. These processes maybe performed by hardware components or may be embodied in computerprogram or machine-executable instructions, which may be used to cause ageneral-purpose or special-purpose processor or logic circuitsprogrammed with the instructions to perform the processes.Alternatively, the processes may be performed by a combination ofhardware and software.

Portions of various embodiments may be provided as a computer programproduct, which may include a computer-readable medium having storedthereon computer program instructions, which may be used to program acomputer (or other electronic devices) for execution by one or moreprocessors to perform a process according to certain embodiments. Thecomputer-readable medium may include, but is not limited to, magneticdisks, optical disks, read-only memory (ROM), random access memory(RAM), erasable programmable read-only memory (EPROM),electrically-erasable programmable read-only memory (EEPROM), magneticor optical cards, flash memory, or other type of computer-readablemedium suitable for storing electronic instructions. Moreover,embodiments may also be downloaded as a computer program product,wherein the program may be transferred from a remote computer to arequesting computer. In some embodiments, a non-transitorycomputer-readable storage medium has stored thereon data representingsequences of instructions that, when executed by a processor, cause theprocessor to perform certain operations.

Many of the methods are described in their most basic form, butprocesses can be added to or deleted from any of the methods andinformation can be added or subtracted from any of the describedmessages without departing from the basic scope of the presentembodiments. It will be apparent to those skilled in the art that manyfurther modifications and adaptations can be made. The particularembodiments are not provided to limit the concept but to illustrate it.The scope of the embodiments is not to be determined by the specificexamples provided above but only by the claims below.

If it is said that an element “A” is coupled to or with element “B,”element A may be directly coupled to element B or be indirectly coupledthrough, for example, element C. When the specification or claims statethat a component, feature, structure, process, or characteristic A“causes” a component, feature, structure, process, or characteristic B,it means that “A” is at least a partial cause of “B” but that there mayalso be at least one other component, feature, structure, process, orcharacteristic that assists in causing “B.” If the specificationindicates that a component, feature, structure, process, orcharacteristic “may”, “might”, or “could” be included, that particularcomponent, feature, structure, process, or characteristic is notrequired to be included. If the specification or claim refers to “a” or“an” element, this does not mean there is only one of the describedelements.

An embodiment is an implementation or example. Reference in thespecification to “an embodiment,” “one embodiment,” “some embodiments,”or “other embodiments” means that a particular feature, structure, orcharacteristic described in connection with the embodiments is includedin at least some embodiments, but not necessarily all embodiments. Thevarious appearances of “an embodiment,” “one embodiment,” or “someembodiments” are not necessarily all referring to the same embodiments.It should be appreciated that in the foregoing description of exemplaryembodiments, various features are sometimes grouped together in a singleembodiment, figure, or description thereof for the purpose ofstreamlining the disclosure and aiding in the understanding of one ormore of the various novel aspects. This method of disclosure, however,is not to be interpreted as reflecting an intention that the claimedembodiments requires more features than are expressly recited in eachclaim. Rather, as the following claims reflect, novel aspects lie inless than all features of a single foregoing disclosed embodiment. Thus,the claims are hereby expressly incorporated into this description, witheach claim standing on its own as a separate embodiment.

What is claimed is:
 1. A system comprising: a plurality of receivers,including a first receiver at a first location and a second receiver ata second location, each of the plurality of receivers including one ormore receiver antennas to receive polarized radio signals; a transmitterlocated at a third location to transmit a plurality of polarized radiosignals, the transmitter including antennas for transmission of a firstsignal at a first polarization direction and transmission of a secondsignal at a second, different polarization direction; and a processingsystem to receive channel state information from each of the pluralityof receivers, and to track a plurality of individuals utilizing thereceived channel state information; wherein the tracking of theplurality of individuals is based at least in part on analysis of apolarization parameter and a set of location parameters that aregenerated for each of plurality of reflection paths based on the channelstate information for each of the plurality of receivers.
 2. The systemof claim 1, wherein the antennas of the transmitter include a firsttransmitter antenna to transmit the first signal at the firstpolarization and a second transmitter antenna to transmit the secondsignal at the second polarization, wherein the first transmitter antennaand the second transmitter antenna are co-located.
 3. The system ofclaim 2, wherein the first polarization direction is horizontalpolarization and the second polarization direction is verticalpolarization.
 4. The system of claim 1, wherein the one or more receiverantennas of each of the plurality of receivers includes uniform lineararray (ULA) of antennas.
 5. The system of claim 1, wherein theprocessing system includes one of: a dedicated processing server; or anetwork controller.
 6. The system of claim 1, wherein the set oflocation parameters includes: time of flight (ToF); angle of arrival(AoA); and dynamic frequency selection (DFS).
 7. The system of claim 1,wherein the polarization parameter is a polarization axial ratio angle.8. The system of claim 1, wherein the tracking of the plurality ofindividuals includes: combining of reflection paths at each of theplurality of receivers that have a closest polarization parameter toidentify reflective targets; and localization of the identifiedreflective targets using the set of location parameters.
 9. The systemof claim 1, wherein the tracking of the plurality of receivers includesapplication of a machine learning estimation framework to estimate oneor more parameters for the plurality of reflective paths.
 10. The systemof claim 1, wherein each of the transmitter and plurality of receiversis a Wi-Fi access point.
 11. One or more non-transitorycomputer-readable storage mediums having stored thereon executablecomputer program instructions that, when executed by one or moreprocessors, cause the one or more processors to perform operationscomprising: receiving channel state information for received signalsfrom each of a plurality of receivers, the plurality of receiversincluding a first receiver and a second receiver, the received signalsbeing reflections of a first signal transmitted at a first polarizationdirection and a second signal transmitted at a second polarizationdirection; determining a polarization parameter and a set of locationparameters for each of a plurality of reflection paths at each of theplurality of receivers; combining reflection paths across the pluralityof receivers to identify reflections for a plurality of persons; anddetermining a location for each of the plurality of persons based atleast in part on the set of location parameters for each of theplurality of reflection paths at each of the plurality of receivers. 12.The one or more mediums of claim 11, wherein the first polarizationdirection is horizontal polarization and the second polarizationdirection is vertical polarization.
 13. The one or more mediums of claim11, wherein the set of location parameters includes: time of flight(ToF); angle of arrival (AoA); and dynamic frequency selection (DFS).14. The one or more mediums of claim 11, wherein the polarizationparameter is generated by determining a ratio of amplitudes of thepolarized signals in the first polarization direction and in the secondpolarization direction.
 15. The one or more mediums of claim 11, furthercomprising executable computer program instructions that, when executedby the one or more processors, cause the one or more processors toperform operations comprising: combining reflection paths at each of theplurality of receivers that have a closest polarization parameter toidentify reflective targets; and localization of the identifiedreflective targets using the set of location parameters.
 16. The one ormore mediums of claim 11, further comprising executable computer programinstructions that, when executed by the one or more processors, causethe one or more processors to perform operations comprising: estimatingone or more parameters for the plurality of reflective paths utilizing amachine learning estimation framework.
 17. A method for passivemulti-person location tracking utilizing signal polarization comprising:transmitting polarized radio signals from a Wi-Fi transmitter at a firstlocation, the polarized signals including a first signal transmittedwith a horizontal polarization and a second signal transmitted withvertical polarization, the signals being transmitted in a trackingenvironment; receiving reflected signals from the tracking environmentat a first receiver at a first location and a second receiver at asecond location, each of the first and second receivers including one ormore receiver antennas to receive polarized radio signals; receivingchannel state information at a location tracking server from the firstreceiver and the second receiver; generating a polarization parameterand a set of location parameters based on the channel state informationreceived from the first receiver and the second receiver; combining ofreflection paths at first and second receivers that have a closestpolarization parameter to identify a plurality of persons in thetracking environment; and determining a location of each of plurality ofpersons in the tracking environment using the set of locationparameters.
 18. The method of claim 17, wherein the polarized signalsare transmitted utilizing a first transmitter antenna to transmit thefirst signal at the horizontal polarization and a second transmitterantenna to transmit the second signal at the vertical polarization, thefirst transmitter antenna and the second transmitter antenna beingco-located, and wherein each of the first and second receivers includesuniform linear array (ULA) of circular polarization antennas.
 19. Themethod of claim 17, wherein the set of location parameters includes:time of flight (ToF); angle of arrival (AoA); and dynamic frequencyselection (DFS).
 20. The method of claim 17, wherein the polarizationparameter is a polarization axial ratio angle.